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Artificial Neural Network for Daily Low Stream Flow Rate Prediction of Perigiali Stream, Kavala City, NE Greece
Open AccessProceedings

Forecasting Low Stream Flow Rate Using Monte—Carlo Simulation of Perigiali Stream, Kavala City, NE Greece

1
Department of Civil Engineering, Democritus University of Thrace, Kimmeria Campus, 67100 Xanthi, Greece
2
Department of Mechanical Engineering, Eastern Macedonia & Thrace Institute of Technology, 65404 Kavala, Greece
*
Author to whom correspondence should be addressed.
Presented at the 3rd EWaS International Conference on “Insights on the Water-Energy-Food Nexus”, Lefkada Island, Greece, 27–30 June 2018.
Proceedings 2018, 2(11), 580; https://doi.org/10.3390/proceedings2110580
Published: 20 August 2018
(This article belongs to the Proceedings of EWaS3 2018)
A small number of scientific research studies with reference to extremely low flow conditions, have been conducted in Greece, so far. Predicting future low stream flow rate values is an essential and of paramount importance task when compiling watershed and drought management plans, designing water reservoirs and general hydraulic works capacity, calculating hydrological and drought low flow values, separating groundwater base flow and storm flow of storm hydrographs etc. The Monte-Carlo simulation method generates multiple attempts to define the anticipated value of a random (hydrological in this specific case) variable. The present study compiles, correspondingly, artificial low stream flow time series of both the same part of the year (2016) as well as a part of the calendar year (2017), based on the stream flow data observed during the same two different interval periods of the years 2016 and 2017, using a 3-inches U.S.G.S. modified portable Parshall flume, a 3-inches conventional portable Parshall flume, a 3-inches portable Montana (short Parshall) flume and a 90° V-notched triangular shaped sharp crested portable weir plate. The recorded data were plotted against the fitted one and the results were demonstrated through interactive tables providing us the ability to effectively evaluate the simulation procedure performance. Finally, we plot the observed against the calculated low stream flow rate data, compiling a log-log scale chart which provides a better visualization of the discrepancy ratio statistical performance metric and calculate statistics featuring the comparison between the recorded and the forecasted low stream flow rate data.
Keywords: artificial time series; discrepancy ratio; Monte-Carlo simulation; low flow data; Parshall flume artificial time series; discrepancy ratio; Monte-Carlo simulation; low flow data; Parshall flume
MDPI and ACS Style

Papalaskaris, T.; Panagiotidis, T. Forecasting Low Stream Flow Rate Using Monte—Carlo Simulation of Perigiali Stream, Kavala City, NE Greece. Proceedings 2018, 2, 580. https://doi.org/10.3390/proceedings2110580

AMA Style

Papalaskaris T, Panagiotidis T. Forecasting Low Stream Flow Rate Using Monte—Carlo Simulation of Perigiali Stream, Kavala City, NE Greece. Proceedings. 2018; 2(11):580. https://doi.org/10.3390/proceedings2110580

Chicago/Turabian Style

Papalaskaris, Thomas; Panagiotidis, Theologos. 2018. "Forecasting Low Stream Flow Rate Using Monte—Carlo Simulation of Perigiali Stream, Kavala City, NE Greece" Proceedings 2, no. 11: 580. https://doi.org/10.3390/proceedings2110580

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